{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2656ec0e",
   "metadata": {},
   "source": [
    "# DAY12 贝叶斯优化可视化和随机森林的解读\n",
    "\n",
    "作者：疏锦行\n",
    "\n",
    "作者微信：shujinxing777"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ca9e3f",
   "metadata": {},
   "source": [
    "# 一、元组类型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31b3fa67",
   "metadata": {},
   "source": [
    "为了学习今天的内容，我们学习一下最后一个没提的基本数据类型，元组（tuple）具有以下特点：\n",
    "\n",
    "1. 有序：可以通过索引取出来元素\n",
    "2. 不可变，不可修改\n",
    "3. 可迭代、可切片\n",
    "所以元组适合存储不应被程序意外修改的数据（例如配置常量、数据库记录的字段等）。函数返回多个值时，默认就是以元组的形式返回的。由于元组是不可变的，它可以作为字典的键（List 不可以）。\n",
    "\n",
    "你也会发现元组和字符串性质一样啊，那为什么需要2个数据结构来表达这两个类型么，是因为它们之间的根本区别在于它们内部存储的元素类型\n",
    "\n",
    "- 元组可以存储任意不同类型的数据对象（异构）。例如：整数、浮点数、列表、函数等。----异构容器，类似于表格存储\n",
    "- 字符串只能存储字符（本质上是文本数据，都是字符类型）。---同构序列，文件名存储\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d57490f2",
   "metadata": {},
   "source": [
    "不可变意味着他不具备增删改的步骤，增加就是创建新元组了"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b26548",
   "metadata": {},
   "source": [
    "先看下创建元组的方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ab7018c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始元组: ('张三', 25, 92.5)\n",
      "原始类型: <class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "# 创建元祖\n",
    "# 原始元组：(姓名, 年龄, 成绩)\n",
    "old_tuple = (\"张三\", 25, 92.5)\n",
    "\n",
    "print(f\"原始元组: {old_tuple}\")\n",
    "print(f\"原始类型: {type(old_tuple)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b7a3e07",
   "metadata": {},
   "source": [
    "看下修改元组的方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "297439f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "转换为列表: ['张三', 25, 92.5]\n",
      "列表类型: <class 'list'>\n",
      "修改后的列表: ['张三', 26, 92.5]\n",
      "\n",
      "转换回元组: ('张三', 26, 92.5)\n",
      "最终类型: <class 'tuple'>\n",
      "原元组 (未变): ('张三', 25, 92.5)\n",
      "新元组的年龄: 26\n"
     ]
    }
   ],
   "source": [
    "# 1. 转换为列表 (List)\n",
    "temp_list = list(old_tuple)\n",
    "\n",
    "print(f\"\\n转换为列表: {temp_list}\")\n",
    "print(f\"列表类型: {type(temp_list)}\")\n",
    "\n",
    "# 2. 修改列表中的元素（列表是可变的）\n",
    "# 索引 1 是年龄\n",
    "temp_list[1] = 26\n",
    "\n",
    "print(f\"修改后的列表: {temp_list}\")\n",
    "\n",
    "# 3. 转换回元组 (Tuple)\n",
    "new_tuple = tuple(temp_list)\n",
    "\n",
    "print(f\"\\n转换回元组: {new_tuple}\")\n",
    "print(f\"最终类型: {type(new_tuple)}\")\n",
    "print(f\"原元组 (未变): {old_tuple}\") # 原始元组并未被修改\n",
    "\n",
    "# 验证修改结果\n",
    "print(f\"新元组的年龄: {new_tuple[1]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0708c202",
   "metadata": {},
   "source": [
    "# 二、字典的items方法\n",
    "\n",
    "字典的items方法，这个方法很重要，在后面深度学习的代码中自由度很高，我们会频繁接触到这个方法，我们来介绍下\n",
    "\n",
    "items() 方法是 Python 中 字典 (Dictionary) 对象的一个非常常用的方法。它返回一个由字典中所有 (键, 值) 对 组成的视图对象（View Object）。这个视图对象可以用于迭代字典中的所有键值对。本质这也是python的解包操作的一种，我们后续会有专题重点讲解下解包操作。\n",
    "\n",
    "什么叫视图对象？具有视图特性，返回的对象是动态的。如果原始字典在您获取 items() 视图后发生了变化（例如添加或删除了键值对），视图对象也会实时反映这些变化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0cf5a306",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "参数: n_estimators , 搜索范围: [10, 3000]\n",
      "参数: max_depth , 搜索范围: [3, 500]\n",
      "参数: max_features , 搜索范围: [0.1, 1.0]\n"
     ]
    }
   ],
   "source": [
    "pbounds = {\n",
    "    'n_estimators': (10, 3000), \n",
    "    'max_depth': (3, 500), \n",
    "    'max_features': (0.1, 1.0)\n",
    "}\n",
    "\n",
    "for param, (low, high) in pbounds.items():\n",
    "   print(f\"参数: {param} , 搜索范围: [{low}, {high}]\") # print在输出后自动添加换行符"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d09bc3fe",
   "metadata": {},
   "source": [
    "聪明的你肯定注意到了，这和我们前几天说的enumerate方法非常像，他可以遍历任何可迭代对象，返回索引+元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae60cd78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 1. 遍历列表 (List) ---\n",
      "索引: 0, 元素: 苹果\n",
      "索引: 1, 元素: 香蕉\n",
      "索引: 2, 元素: 樱桃\n",
      "索引: 3, 元素: 日期\n"
     ]
    }
   ],
   "source": [
    "# --- 1. 列表 (List) ---\n",
    "print(\"--- 1. 遍历列表 (List) ---\")\n",
    "my_list = ['苹果', '香蕉', '樱桃', '日期']\n",
    "# enumerate() 默认从索引 0 开始计数\n",
    "for index, item in enumerate(my_list):\n",
    "    print(f\"索引: {index}, 元素: {item}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3cf9c24f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 2. 遍历字符串 (String) ---\n",
      "索引: 0, 字符: P\n",
      "索引: 1, 字符: y\n",
      "索引: 2, 字符: t\n",
      "索引: 3, 字符: h\n",
      "索引: 4, 字符: o\n",
      "索引: 5, 字符: n\n"
     ]
    }
   ],
   "source": [
    "# --- 2. 字符串 (String) ---\n",
    "print(\"--- 2. 遍历字符串 (String) ---\")\n",
    "my_string = \"Python\"\n",
    "for index, char in enumerate(my_string):\n",
    "    print(f\"索引: {index}, 字符: {char}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "71d8c994",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 3. 遍历元组 (Tuple) ---\n",
      "索引: 0, 姓名: 张三\n",
      "索引: 1, 姓名: 李四\n",
      "索引: 2, 姓名: 王五\n"
     ]
    }
   ],
   "source": [
    "print(\"--- 3. 遍历元组 (Tuple) ---\")\n",
    "my_tuple = ('张三', '李四', '王五')\n",
    "for index, name in enumerate(my_tuple):\n",
    "    print(f\"索引: {index}, 姓名: {name}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4616d9a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 4. 遍历字典的键 (Keys) ---\n",
      "索引: 0, 键: A, 对应值: 10\n",
      "索引: 1, 键: B, 对应值: 20\n",
      "索引: 2, 键: C, 对应值: 30\n"
     ]
    }
   ],
   "source": [
    "print(\"--- 4. 遍历字典的键 (Keys) ---\")\n",
    "my_dict_simple = {'A': 10, 'B': 20, 'C': 30}\n",
    "# 默认情况下，直接遍历字典只会得到键\n",
    "for index, key in enumerate(my_dict_simple):\n",
    "    print(f\"索引: {index}, 键: {key}, 对应值: {my_dict_simple[key]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ce2de2a",
   "metadata": {},
   "source": [
    "在python历史中，字典是无序的，Python 3.7 及更高版本，字典正式成为有序的。这意味着字典会记住键的插入顺序，并且在遍历时（包括使用 enumerate() 时），会严格按照这个顺序进行。这也意味着以后常见数据结构只能遇到集合是无需的了。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dbef0a0",
   "metadata": {},
   "source": [
    "实际上下面这items和enumerate联合的写法非常常见"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a02f0ba1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "索引: 0, 键: A, 对应值: 10\n",
      "索引: 1, 键: B, 对应值: 20\n",
      "索引: 2, 键: C, 对应值: 30\n"
     ]
    }
   ],
   "source": [
    "my_dict_simple = {'A': 10, 'B': 20, 'C': 30}\n",
    "\n",
    "# 1. my_dict_simple.items() 返回 ('A', 10), ('B', 20) 等 (键, 值) 元组\n",
    "# 2. enumerate 为这些元组添加索引\n",
    "# 3. 循环中用 index, (key, value) 进行两次解包\n",
    "for index, (key, value) in enumerate(my_dict_simple.items()):\n",
    "    # 键和值通过解包直接获得，无需额外查表\n",
    "    print(f\"索引: {index}, 键: {key}, 对应值: {value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d8cbe08",
   "metadata": {},
   "source": [
    "大家记住这个写法，我们未来会有针对解包的专题，解包是非常非常重要的知识点."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1f525c3",
   "metadata": {},
   "source": [
    "# 三、贝叶斯优化可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50170edc",
   "metadata": {},
   "source": [
    "## 1. 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "44f34c12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入必要的库\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "31be3910",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据形状: (7500, 18)\n",
      "\n",
      "前5行数据:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Home Ownership</th>\n",
       "      <th>Annual Income</th>\n",
       "      <th>Years in current job</th>\n",
       "      <th>Tax Liens</th>\n",
       "      <th>Number of Open Accounts</th>\n",
       "      <th>Years of Credit History</th>\n",
       "      <th>Maximum Open Credit</th>\n",
       "      <th>Number of Credit Problems</th>\n",
       "      <th>Months since last delinquent</th>\n",
       "      <th>Bankruptcies</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Term</th>\n",
       "      <th>Current Loan Amount</th>\n",
       "      <th>Current Credit Balance</th>\n",
       "      <th>Monthly Debt</th>\n",
       "      <th>Credit Score</th>\n",
       "      <th>Credit Default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Own Home</td>\n",
       "      <td>482087.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>685960.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>debt consolidation</td>\n",
       "      <td>Short Term</td>\n",
       "      <td>99999999.0</td>\n",
       "      <td>47386.0</td>\n",
       "      <td>7914.0</td>\n",
       "      <td>749.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Own Home</td>\n",
       "      <td>1025487.0</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>1181730.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>debt consolidation</td>\n",
       "      <td>Long Term</td>\n",
       "      <td>264968.0</td>\n",
       "      <td>394972.0</td>\n",
       "      <td>18373.0</td>\n",
       "      <td>737.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Home Mortgage</td>\n",
       "      <td>751412.0</td>\n",
       "      <td>8 years</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1182434.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>debt consolidation</td>\n",
       "      <td>Short Term</td>\n",
       "      <td>99999999.0</td>\n",
       "      <td>308389.0</td>\n",
       "      <td>13651.0</td>\n",
       "      <td>742.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Own Home</td>\n",
       "      <td>805068.0</td>\n",
       "      <td>6 years</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>147400.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>debt consolidation</td>\n",
       "      <td>Short Term</td>\n",
       "      <td>121396.0</td>\n",
       "      <td>95855.0</td>\n",
       "      <td>11338.0</td>\n",
       "      <td>694.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Rent</td>\n",
       "      <td>776264.0</td>\n",
       "      <td>8 years</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>385836.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>debt consolidation</td>\n",
       "      <td>Short Term</td>\n",
       "      <td>125840.0</td>\n",
       "      <td>93309.0</td>\n",
       "      <td>7180.0</td>\n",
       "      <td>719.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id Home Ownership  Annual Income Years in current job  Tax Liens  \\\n",
       "0   0       Own Home       482087.0                  NaN        0.0   \n",
       "1   1       Own Home      1025487.0            10+ years        0.0   \n",
       "2   2  Home Mortgage       751412.0              8 years        0.0   \n",
       "3   3       Own Home       805068.0              6 years        0.0   \n",
       "4   4           Rent       776264.0              8 years        0.0   \n",
       "\n",
       "   Number of Open Accounts  Years of Credit History  Maximum Open Credit  \\\n",
       "0                     11.0                     26.3             685960.0   \n",
       "1                     15.0                     15.3            1181730.0   \n",
       "2                     11.0                     35.0            1182434.0   \n",
       "3                      8.0                     22.5             147400.0   \n",
       "4                     13.0                     13.6             385836.0   \n",
       "\n",
       "   Number of Credit Problems  Months since last delinquent  Bankruptcies  \\\n",
       "0                        1.0                           NaN           1.0   \n",
       "1                        0.0                           NaN           0.0   \n",
       "2                        0.0                           NaN           0.0   \n",
       "3                        1.0                           NaN           1.0   \n",
       "4                        1.0                           NaN           0.0   \n",
       "\n",
       "              Purpose        Term  Current Loan Amount  \\\n",
       "0  debt consolidation  Short Term           99999999.0   \n",
       "1  debt consolidation   Long Term             264968.0   \n",
       "2  debt consolidation  Short Term           99999999.0   \n",
       "3  debt consolidation  Short Term             121396.0   \n",
       "4  debt consolidation  Short Term             125840.0   \n",
       "\n",
       "   Current Credit Balance  Monthly Debt  Credit Score  Credit Default  \n",
       "0                 47386.0        7914.0         749.0               0  \n",
       "1                394972.0       18373.0         737.0               1  \n",
       "2                308389.0       13651.0         742.0               0  \n",
       "3                 95855.0       11338.0         694.0               0  \n",
       "4                 93309.0        7180.0         719.0               0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "data = pd.read_csv('E:\\\\study\\\\PythonStudy\\\\python60-days-challenge-master\\\\data.csv')\n",
    "print(f\"数据形状: {data.shape}\")\n",
    "print(f\"\\n前5行数据:\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "54850d47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 数据预处理完成！\n",
      "最终特征数量: 32\n"
     ]
    }
   ],
   "source": [
    "# 数据预处理\n",
    "discrete_features = data.select_dtypes(include=['object']).columns.tolist()\n",
    "\n",
    "# Home Ownership 标签编码\n",
    "home_ownership_mapping = {\n",
    "    'Own Home': 1,\n",
    "    'Rent': 2,\n",
    "    'Have Mortgage': 3,\n",
    "    'Home Mortgage': 4\n",
    "}\n",
    "data['Home Ownership'] = data['Home Ownership'].map(home_ownership_mapping)\n",
    "\n",
    "# Years in current job 标签编码\n",
    "years_in_job_mapping = {\n",
    "    '< 1 year': 1, '1 year': 2, '2 years': 3, '3 years': 4, '4 years': 5,\n",
    "    '5 years': 6, '6 years': 7, '7 years': 8, '8 years': 9, '9 years': 10, '10+ years': 11\n",
    "}\n",
    "data['Years in current job'] = data['Years in current job'].map(years_in_job_mapping)\n",
    "\n",
    "# Purpose 独热编码\n",
    "data = pd.get_dummies(data, columns=['Purpose'])\n",
    "data2 = pd.read_csv(\"E:\\\\study\\\\PythonStudy\\\\python60-days-challenge-master\\\\data.csv\")\n",
    "list_final = [i for i in data.columns if i not in data2.columns]\n",
    "for i in list_final:\n",
    "    data[i] = data[i].astype(int)\n",
    "\n",
    "# Term 0-1 映射\n",
    "term_mapping = {'Short Term': 0, 'Long Term': 1}\n",
    "data['Term'] = data['Term'].map(term_mapping)\n",
    "data.rename(columns={'Term': 'Long Term'}, inplace=True)\n",
    "\n",
    "# 连续特征用众数补全\n",
    "continuous_features = data.select_dtypes(include=['int64', 'float64']).columns.tolist()\n",
    "for feature in continuous_features:\n",
    "    mode_value = data[feature].mode()[0]\n",
    "    data[feature].fillna(mode_value, inplace=True)\n",
    "\n",
    "print(\"✅ 数据预处理完成！\")\n",
    "print(f\"最终特征数量: {data.shape[1]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "517e1111",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集大小: (6000, 31)\n",
      "测试集大小: (1500, 31)\n",
      "类别分布:\n",
      "Credit Default\n",
      "0    4328\n",
      "1    1672\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 划分训练集和测试集\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = data.drop(['Credit Default'], axis=1)\n",
    "y = data['Credit Default']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "print(f\"训练集大小: {X_train.shape}\")\n",
    "print(f\"测试集大小: {X_test.shape}\")\n",
    "print(f\"类别分布:\\n{y_train.value_counts()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c314f16e",
   "metadata": {},
   "source": [
    "## 2. 基础贝叶斯优化\n",
    "\n",
    "首先安装必要的库（如果还未安装）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0aadfab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install bayesian-optimization -i https://mirrors.aliyun.com/pypi/simple/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98ca1591",
   "metadata": {},
   "source": [
    "昨天我们介绍了贝叶斯优化的实现形式，sklearn、贝叶斯优化库、optuna都可以。我们今天选择贝叶斯优化库，他的自由度大很多。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5e54b391",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  n_estimators        : [   10.0,  3000.0]  (范围:  2990.0)\n",
      "  max_depth           : [    3.0,   500.0]  (范围:   497.0)\n",
      "  min_samples_split   : [    2.0,   200.0]  (范围:   198.0)\n",
      "  min_samples_leaf    : [    1.0,   100.0]  (范围:    99.0)\n",
      "  max_features        : [    0.1,     1.0]  (范围:     0.9)\n"
     ]
    }
   ],
   "source": [
    "from bayes_opt import BayesianOptimization\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import time\n",
    "\n",
    "# 定义目标函数\n",
    "def rf_eval(n_estimators, max_depth, min_samples_split, min_samples_leaf, max_features):\n",
    "    \"\"\"\n",
    "    目标函数：评估随机森林在给定参数下的性能\n",
    "    BayesianOptimization 会最大化这个函数的返回值\n",
    "    \n",
    "    参数说明：\n",
    "    - n_estimators: 树的数量（越多越好，但会增加计算时间）\n",
    "    - max_depth: 树的最大深度（太浅欠拟合，太深过拟合）\n",
    "    - min_samples_split: 分裂所需最小样本数（控制树的生长）\n",
    "    - min_samples_leaf: 叶节点最小样本数（防止过拟合）\n",
    "    - max_features: 特征采样比例（增加随机性，防止过拟合）\n",
    "    \"\"\"\n",
    "    # 将连续参数转换为整数\n",
    "    n_estimators = int(n_estimators)\n",
    "    max_depth = int(max_depth)\n",
    "    min_samples_split = int(min_samples_split)\n",
    "    min_samples_leaf = int(min_samples_leaf)\n",
    "    # max_features 保持浮点数\n",
    "    \n",
    "    # 创建模型\n",
    "    model = RandomForestClassifier(\n",
    "        n_estimators=n_estimators,\n",
    "        max_depth=max_depth,\n",
    "        min_samples_split=min_samples_split,\n",
    "        min_samples_leaf=min_samples_leaf,\n",
    "        max_features=max_features,  \n",
    "        random_state=42,\n",
    "        n_jobs=-1\n",
    "    )\n",
    "    \n",
    "    # 5折交叉验证\n",
    "    scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')\n",
    "    return np.mean(scores)\n",
    "\n",
    "# 定义参数搜索空间（扩大10倍！超大搜索空间）\n",
    "pbounds = {\n",
    "    'n_estimators': (10, 3000),          # 从10到3000棵树\n",
    "    'max_depth': (3, 500),               # 从3到500\n",
    "    'min_samples_split': (2, 200),       # 从2到200\n",
    "    'min_samples_leaf': (1, 100),        # 从1到100\n",
    "    'max_features': (0.1, 1.0)           # 从10%到100%\n",
    "}\n",
    "\n",
    "\n",
    "for param, (low, high) in pbounds.items(): # items方法返回字典的键值对\n",
    "    range_size = high - low\n",
    "    print(f\"  {param:20s}: [{low:7.1f}, {high:7.1f}]  (范围: {range_size:7.1f})\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98fe642e",
   "metadata": {},
   "source": [
    "## 3. 详细输出与迭代过程\n",
    "\n",
    "运行贝叶斯优化，查看每次迭代的详细信息："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "514195a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   iter    |  target   | n_esti... | max_depth | min_sa... | min_sa... | max_fe... |\n",
      "-------------------------------------------------------------------------------------\n",
      "| \u001b[39m1        \u001b[39m | \u001b[39m0.7745   \u001b[39m | \u001b[39m1129.8749\u001b[39m | \u001b[39m475.50501\u001b[39m | \u001b[39m146.93480\u001b[39m | \u001b[39m60.267189\u001b[39m | \u001b[39m0.2404167\u001b[39m |\n",
      "| \u001b[35m2        \u001b[39m | \u001b[35m0.7803333\u001b[39m | \u001b[35m476.42361\u001b[39m | \u001b[35m31.867555\u001b[39m | \u001b[35m173.50287\u001b[39m | \u001b[35m60.510386\u001b[39m | \u001b[35m0.7372653\u001b[39m |\n",
      "| \u001b[39m3        \u001b[39m | \u001b[39m0.7778333\u001b[39m | \u001b[39m71.547637\u001b[39m | \u001b[39m485.04519\u001b[39m | \u001b[39m166.82364\u001b[39m | \u001b[39m22.021571\u001b[39m | \u001b[39m0.2636424\u001b[39m |\n",
      "| \u001b[35m4        \u001b[39m | \u001b[35m0.7818333\u001b[39m | \u001b[35m558.37948\u001b[39m | \u001b[35m154.20839\u001b[39m | \u001b[35m105.90177\u001b[39m | \u001b[35m43.762556\u001b[39m | \u001b[35m0.3621062\u001b[39m |\n",
      "| \u001b[35m5        \u001b[39m | \u001b[35m0.7823333\u001b[39m | \u001b[35m1839.4401\u001b[39m | \u001b[35m72.328448\u001b[39m | \u001b[35m59.844640\u001b[39m | \u001b[35m37.269822\u001b[39m | \u001b[35m0.5104629\u001b[39m |\n",
      "| \u001b[39m6        \u001b[39m | \u001b[39m0.7728333\u001b[39m | \u001b[39m2357.6761\u001b[39m | \u001b[39m102.23786\u001b[39m | \u001b[39m103.81841\u001b[39m | \u001b[39m59.649042\u001b[39m | \u001b[39m0.1418053\u001b[39m |\n",
      "| \u001b[39m7        \u001b[39m | \u001b[39m0.778    \u001b[39m | \u001b[39m1826.5591\u001b[39m | \u001b[39m87.750489\u001b[39m | \u001b[39m14.880215\u001b[39m | \u001b[39m94.939668\u001b[39m | \u001b[39m0.9690688\u001b[39m |\n",
      "| \u001b[35m8        \u001b[39m | \u001b[35m0.7825   \u001b[39m | \u001b[35m2427.1080\u001b[39m | \u001b[35m154.39304\u001b[39m | \u001b[35m21.339078\u001b[39m | \u001b[35m68.739069\u001b[39m | \u001b[35m0.4961372\u001b[39m |\n",
      "| \u001b[39m9        \u001b[39m | \u001b[39m0.7785   \u001b[39m | \u001b[39m374.89432\u001b[39m | \u001b[39m249.10292\u001b[39m | \u001b[39m8.8089271\u001b[39m | \u001b[39m91.022719\u001b[39m | \u001b[39m0.3329019\u001b[39m |\n",
      "| \u001b[39m10       \u001b[39m | \u001b[39m0.7783333\u001b[39m | \u001b[39m1990.9416\u001b[39m | \u001b[39m157.92040\u001b[39m | \u001b[39m104.97346\u001b[39m | \u001b[39m55.124317\u001b[39m | \u001b[39m0.2663690\u001b[39m |\n",
      "| \u001b[35m11       \u001b[39m | \u001b[35m0.7830000\u001b[39m | \u001b[35m2909.0580\u001b[39m | \u001b[35m388.24101\u001b[39m | \u001b[35m188.02079\u001b[39m | \u001b[35m89.587907\u001b[39m | \u001b[35m0.6381099\u001b[39m |\n",
      "| \u001b[35m12       \u001b[39m | \u001b[35m0.7836666\u001b[39m | \u001b[35m2766.4039\u001b[39m | \u001b[35m46.980773\u001b[39m | \u001b[35m40.804606\u001b[39m | \u001b[35m5.4775016\u001b[39m | \u001b[35m0.3927972\u001b[39m |\n",
      "| \u001b[39m13       \u001b[39m | \u001b[39m0.7796666\u001b[39m | \u001b[39m1172.1450\u001b[39m | \u001b[39m137.86046\u001b[39m | \u001b[39m166.09002\u001b[39m | \u001b[39m36.318579\u001b[39m | \u001b[39m0.3528410\u001b[39m |\n",
      "| \u001b[39m14       \u001b[39m | \u001b[39m0.7798333\u001b[39m | \u001b[39m1632.6612\u001b[39m | \u001b[39m73.039339\u001b[39m | \u001b[39m160.83500\u001b[39m | \u001b[39m8.3805137\u001b[39m | \u001b[39m0.9881982\u001b[39m |\n",
      "| \u001b[39m15       \u001b[39m | \u001b[39m0.7804999\u001b[39m | \u001b[39m2319.0118\u001b[39m | \u001b[39m101.76169\u001b[39m | \u001b[39m3.0933791\u001b[39m | \u001b[39m81.730681\u001b[39m | \u001b[39m0.7361716\u001b[39m |\n",
      "| \u001b[39m16       \u001b[39m | \u001b[39m0.776    \u001b[39m | \u001b[39m2189.7314\u001b[39m | \u001b[39m386.32136\u001b[39m | \u001b[39m16.660841\u001b[39m | \u001b[39m36.488107\u001b[39m | \u001b[39m0.2042821\u001b[39m |\n",
      "| \u001b[39m17       \u001b[39m | \u001b[39m0.7833333\u001b[39m | \u001b[39m2590.6792\u001b[39m | \u001b[39m312.77916\u001b[39m | \u001b[39m67.517808\u001b[39m | \u001b[39m7.2922766\u001b[39m | \u001b[39m0.3798840\u001b[39m |\n",
      "| \u001b[39m18       \u001b[39m | \u001b[39m0.7823333\u001b[39m | \u001b[39m982.29813\u001b[39m | \u001b[39m365.61427\u001b[39m | \u001b[39m128.23637\u001b[39m | \u001b[39m88.834061\u001b[39m | \u001b[39m0.5249934\u001b[39m |\n",
      "| \u001b[39m19       \u001b[39m | \u001b[39m0.7808333\u001b[39m | \u001b[39m367.58679\u001b[39m | \u001b[39m357.48265\u001b[39m | \u001b[39m152.63543\u001b[39m | \u001b[39m56.566442\u001b[39m | \u001b[39m0.7938704\u001b[39m |\n",
      "| \u001b[39m20       \u001b[39m | \u001b[39m0.7806666\u001b[39m | \u001b[39m1486.4488\u001b[39m | \u001b[39m262.79821\u001b[39m | \u001b[39m86.653121\u001b[39m | \u001b[39m3.5164935\u001b[39m | \u001b[39m0.1971022\u001b[39m |\n",
      "| \u001b[39m21       \u001b[39m | \u001b[39m0.7756666\u001b[39m | \u001b[39m2425.0300\u001b[39m | \u001b[39m157.44856\u001b[39m | \u001b[39m26.743786\u001b[39m | \u001b[39m58.528349\u001b[39m | \u001b[39m0.2327190\u001b[39m |\n",
      "| \u001b[39m22       \u001b[39m | \u001b[39m0.7823333\u001b[39m | \u001b[39m317.08089\u001b[39m | \u001b[39m399.15715\u001b[39m | \u001b[39m24.720105\u001b[39m | \u001b[39m61.174421\u001b[39m | \u001b[39m0.5006357\u001b[39m |\n",
      "| \u001b[39m23       \u001b[39m | \u001b[39m0.7735000\u001b[39m | \u001b[39m2175.1790\u001b[39m | \u001b[39m172.94266\u001b[39m | \u001b[39m82.673729\u001b[39m | \u001b[39m62.461772\u001b[39m | \u001b[39m0.1659964\u001b[39m |\n",
      "| \u001b[39m24       \u001b[39m | \u001b[39m0.7798333\u001b[39m | \u001b[39m530.83893\u001b[39m | \u001b[39m39.948804\u001b[39m | \u001b[39m156.84141\u001b[39m | \u001b[39m68.955501\u001b[39m | \u001b[39m0.8188521\u001b[39m |\n",
      "| \u001b[39m25       \u001b[39m | \u001b[39m0.7823333\u001b[39m | \u001b[39m125.79174\u001b[39m | \u001b[39m279.13058\u001b[39m | \u001b[39m98.416484\u001b[39m | \u001b[39m18.166317\u001b[39m | \u001b[39m0.7781925\u001b[39m |\n",
      "| \u001b[39m26       \u001b[39m | \u001b[39m0.7805   \u001b[39m | \u001b[39m1943.5744\u001b[39m | \u001b[39m274.68149\u001b[39m | \u001b[39m28.783133\u001b[39m | \u001b[39m68.150995\u001b[39m | \u001b[39m0.7199743\u001b[39m |\n",
      "| \u001b[39m27       \u001b[39m | \u001b[39m0.7796666\u001b[39m | \u001b[39m1979.4017\u001b[39m | \u001b[39m459.09935\u001b[39m | \u001b[39m162.55494\u001b[39m | \u001b[39m30.051406\u001b[39m | \u001b[39m0.7814428\u001b[39m |\n",
      "| \u001b[39m28       \u001b[39m | \u001b[39m0.7818333\u001b[39m | \u001b[39m513.28522\u001b[39m | \u001b[39m310.94902\u001b[39m | \u001b[39m163.56804\u001b[39m | \u001b[39m83.030768\u001b[39m | \u001b[39m0.4537288\u001b[39m |\n",
      "| \u001b[39m29       \u001b[39m | \u001b[39m0.7816666\u001b[39m | \u001b[39m298.73901\u001b[39m | \u001b[39m347.12283\u001b[39m | \u001b[39m59.999680\u001b[39m | \u001b[39m35.549478\u001b[39m | \u001b[39m0.4592652\u001b[39m |\n",
      "| \u001b[39m30       \u001b[39m | \u001b[39m0.7793333\u001b[39m | \u001b[39m519.01956\u001b[39m | \u001b[39m99.603760\u001b[39m | \u001b[39m8.9862463\u001b[39m | \u001b[39m63.792105\u001b[39m | \u001b[39m0.9334241\u001b[39m |\n",
      "| \u001b[39m31       \u001b[39m | \u001b[39m0.7781666\u001b[39m | \u001b[39m80.703036\u001b[39m | \u001b[39m305.75024\u001b[39m | \u001b[39m198.73222\u001b[39m | \u001b[39m44.662866\u001b[39m | \u001b[39m0.8430691\u001b[39m |\n",
      "| \u001b[39m32       \u001b[39m | \u001b[39m0.7796666\u001b[39m | \u001b[39m2906.4182\u001b[39m | \u001b[39m197.65251\u001b[39m | \u001b[39m145.00770\u001b[39m | \u001b[39m6.4047592\u001b[39m | \u001b[39m0.9400330\u001b[39m |\n",
      "| \u001b[39m33       \u001b[39m | \u001b[39m0.7818333\u001b[39m | \u001b[39m1153.4739\u001b[39m | \u001b[39m376.22945\u001b[39m | \u001b[39m64.166821\u001b[39m | \u001b[39m1.1935861\u001b[39m | \u001b[39m0.5656587\u001b[39m |\n",
      "| \u001b[39m34       \u001b[39m | \u001b[39m0.7793333\u001b[39m | \u001b[39m948.23272\u001b[39m | \u001b[39m188.22702\u001b[39m | \u001b[39m131.39722\u001b[39m | \u001b[39m1.0806612\u001b[39m | \u001b[39m0.1948411\u001b[39m |\n",
      "| \u001b[39m35       \u001b[39m | \u001b[39m0.7803333\u001b[39m | \u001b[39m2554.5726\u001b[39m | \u001b[39m248.93796\u001b[39m | \u001b[39m24.845931\u001b[39m | \u001b[39m71.106722\u001b[39m | \u001b[39m0.8517691\u001b[39m |\n",
      "| \u001b[39m36       \u001b[39m | \u001b[39m0.7835   \u001b[39m | \u001b[39m424.37459\u001b[39m | \u001b[39m63.396902\u001b[39m | \u001b[39m22.342181\u001b[39m | \u001b[39m26.149522\u001b[39m | \u001b[39m0.5716183\u001b[39m |\n",
      "| \u001b[39m37       \u001b[39m | \u001b[39m0.7798333\u001b[39m | \u001b[39m853.16312\u001b[39m | \u001b[39m36.296215\u001b[39m | \u001b[39m193.11443\u001b[39m | \u001b[39m12.060599\u001b[39m | \u001b[39m0.3533714\u001b[39m |\n",
      "| \u001b[39m38       \u001b[39m | \u001b[39m0.7813333\u001b[39m | \u001b[39m1403.4459\u001b[39m | \u001b[39m287.47407\u001b[39m | \u001b[39m38.708242\u001b[39m | \u001b[39m3.2984263\u001b[39m | \u001b[39m0.4907944\u001b[39m |\n",
      "| \u001b[35m39       \u001b[39m | \u001b[35m0.7844999\u001b[39m | \u001b[35m2537.5609\u001b[39m | \u001b[35m67.559265\u001b[39m | \u001b[35m14.338645\u001b[39m | \u001b[35m21.314120\u001b[39m | \u001b[35m0.5955760\u001b[39m |\n",
      "| \u001b[39m40       \u001b[39m | \u001b[39m0.7818333\u001b[39m | \u001b[39m573.60971\u001b[39m | \u001b[39m114.23228\u001b[39m | \u001b[39m132.24189\u001b[39m | \u001b[39m45.272906\u001b[39m | \u001b[39m0.6922839\u001b[39m |\n",
      "| \u001b[39m41       \u001b[39m | \u001b[39m0.7826666\u001b[39m | \u001b[39m753.91044\u001b[39m | \u001b[39m76.401126\u001b[39m | \u001b[39m68.771433\u001b[39m | \u001b[39m49.325087\u001b[39m | \u001b[39m0.4916381\u001b[39m |\n",
      "| \u001b[39m42       \u001b[39m | \u001b[39m0.7823333\u001b[39m | \u001b[39m1473.3739\u001b[39m | \u001b[39m35.232747\u001b[39m | \u001b[39m107.51187\u001b[39m | \u001b[39m11.949661\u001b[39m | \u001b[39m0.8845080\u001b[39m |\n",
      "| \u001b[39m43       \u001b[39m | \u001b[39m0.7813333\u001b[39m | \u001b[39m75.769845\u001b[39m | \u001b[39m169.91039\u001b[39m | \u001b[39m140.05542\u001b[39m | \u001b[39m18.378717\u001b[39m | \u001b[39m0.8904848\u001b[39m |\n",
      "| \u001b[39m44       \u001b[39m | \u001b[39m0.7735000\u001b[39m | \u001b[39m1934.7092\u001b[39m | \u001b[39m117.78365\u001b[39m | \u001b[39m30.335632\u001b[39m | \u001b[39m82.043426\u001b[39m | \u001b[39m0.2200359\u001b[39m |\n",
      "| \u001b[35m45       \u001b[39m | \u001b[35m0.7846666\u001b[39m | \u001b[35m1133.3085\u001b[39m | \u001b[35m260.67839\u001b[39m | \u001b[35m3.5076515\u001b[39m | \u001b[35m17.975101\u001b[39m | \u001b[35m0.6719520\u001b[39m |\n",
      "| \u001b[39m46       \u001b[39m | \u001b[39m0.7825000\u001b[39m | \u001b[39m2623.9015\u001b[39m | \u001b[39m397.73205\u001b[39m | \u001b[39m125.71576\u001b[39m | \u001b[39m65.187069\u001b[39m | \u001b[39m0.5083534\u001b[39m |\n",
      "| \u001b[39m47       \u001b[39m | \u001b[39m0.7788333\u001b[39m | \u001b[39m1156.5189\u001b[39m | \u001b[39m17.521647\u001b[39m | \u001b[39m170.39534\u001b[39m | \u001b[39m96.298122\u001b[39m | \u001b[39m0.9298774\u001b[39m |\n",
      "| \u001b[39m48       \u001b[39m | \u001b[39m0.7811666\u001b[39m | \u001b[39m1901.3568\u001b[39m | \u001b[39m60.950741\u001b[39m | \u001b[39m39.306505\u001b[39m | \u001b[39m47.619620\u001b[39m | \u001b[39m0.6659724\u001b[39m |\n",
      "| \u001b[39m49       \u001b[39m | \u001b[39m0.7793333\u001b[39m | \u001b[39m438.99674\u001b[39m | \u001b[39m453.66489\u001b[39m | \u001b[39m195.26912\u001b[39m | \u001b[39m59.794469\u001b[39m | \u001b[39m0.7990364\u001b[39m |\n",
      "| \u001b[39m50       \u001b[39m | \u001b[39m0.7806666\u001b[39m | \u001b[39m2720.6271\u001b[39m | \u001b[39m332.73419\u001b[39m | \u001b[39m74.434419\u001b[39m | \u001b[39m54.867846\u001b[39m | \u001b[39m0.5900542\u001b[39m |\n",
      "| \u001b[39m51       \u001b[39m | \u001b[39m0.7736666\u001b[39m | \u001b[39m1320.1037\u001b[39m | \u001b[39m3.4521495\u001b[39m | \u001b[39m157.78656\u001b[39m | \u001b[39m37.572691\u001b[39m | \u001b[39m0.9492811\u001b[39m |\n",
      "| \u001b[39m52       \u001b[39m | \u001b[39m0.7801666\u001b[39m | \u001b[39m971.15043\u001b[39m | \u001b[39m284.53894\u001b[39m | \u001b[39m155.11072\u001b[39m | \u001b[39m17.331234\u001b[39m | \u001b[39m0.8099888\u001b[39m |\n",
      "| \u001b[39m53       \u001b[39m | \u001b[39m0.7813333\u001b[39m | \u001b[39m669.90372\u001b[39m | \u001b[39m334.70999\u001b[39m | \u001b[39m69.079472\u001b[39m | \u001b[39m82.004227\u001b[39m | \u001b[39m0.7211400\u001b[39m |\n",
      "| \u001b[39m54       \u001b[39m | \u001b[39m0.7811666\u001b[39m | \u001b[39m1203.4759\u001b[39m | \u001b[39m209.39405\u001b[39m | \u001b[39m8.8230706\u001b[39m | \u001b[39m41.794833\u001b[39m | \u001b[39m0.4569208\u001b[39m |\n",
      "| \u001b[39m55       \u001b[39m | \u001b[39m0.7831666\u001b[39m | \u001b[39m2571.7657\u001b[39m | \u001b[39m466.37838\u001b[39m | \u001b[39m140.78362\u001b[39m | \u001b[39m69.484479\u001b[39m | \u001b[39m0.4865115\u001b[39m |\n",
      "| \u001b[39m56       \u001b[39m | \u001b[39m0.7813333\u001b[39m | \u001b[39m1870.3275\u001b[39m | \u001b[39m314.75127\u001b[39m | \u001b[39m30.133635\u001b[39m | \u001b[39m50.071866\u001b[39m | \u001b[39m0.6024418\u001b[39m |\n",
      "| \u001b[39m57       \u001b[39m | \u001b[39m0.7813333\u001b[39m | \u001b[39m1201.4225\u001b[39m | \u001b[39m149.06964\u001b[39m | \u001b[39m56.282650\u001b[39m | \u001b[39m52.547336\u001b[39m | \u001b[39m0.8794528\u001b[39m |\n",
      "| \u001b[39m58       \u001b[39m | \u001b[39m0.781    \u001b[39m | \u001b[39m524.06144\u001b[39m | \u001b[39m469.31379\u001b[39m | \u001b[39m130.58694\u001b[39m | \u001b[39m66.431875\u001b[39m | \u001b[39m0.7377440\u001b[39m |\n",
      "| \u001b[39m59       \u001b[39m | \u001b[39m0.7803333\u001b[39m | \u001b[39m273.30264\u001b[39m | \u001b[39m234.01320\u001b[39m | \u001b[39m145.95364\u001b[39m | \u001b[39m46.354749\u001b[39m | \u001b[39m0.8357447\u001b[39m |\n",
      "| \u001b[39m60       \u001b[39m | \u001b[39m0.7801666\u001b[39m | \u001b[39m1087.5393\u001b[39m | \u001b[39m125.91686\u001b[39m | \u001b[39m130.08865\u001b[39m | \u001b[39m67.682628\u001b[39m | \u001b[39m0.9319276\u001b[39m |\n",
      "| \u001b[39m61       \u001b[39m | \u001b[39m0.7788333\u001b[39m | \u001b[39m1048.5384\u001b[39m | \u001b[39m372.84489\u001b[39m | \u001b[39m91.940050\u001b[39m | \u001b[39m88.254461\u001b[39m | \u001b[39m0.3850228\u001b[39m |\n",
      "| \u001b[39m62       \u001b[39m | \u001b[39m0.7771666\u001b[39m | \u001b[39m1328.4268\u001b[39m | \u001b[39m153.80915\u001b[39m | \u001b[39m76.285990\u001b[39m | \u001b[39m79.306738\u001b[39m | \u001b[39m0.2910777\u001b[39m |\n",
      "| \u001b[39m63       \u001b[39m | \u001b[39m0.775    \u001b[39m | \u001b[39m747.27103\u001b[39m | \u001b[39m311.56030\u001b[39m | \u001b[39m121.16394\u001b[39m | \u001b[39m84.193188\u001b[39m | \u001b[39m0.2901180\u001b[39m |\n",
      "| \u001b[39m64       \u001b[39m | \u001b[39m0.7816666\u001b[39m | \u001b[39m2480.0412\u001b[39m | \u001b[39m244.49159\u001b[39m | \u001b[39m42.200987\u001b[39m | \u001b[39m33.311390\u001b[39m | \u001b[39m0.4148590\u001b[39m |\n",
      "| \u001b[39m65       \u001b[39m | \u001b[39m0.7815000\u001b[39m | \u001b[39m117.64791\u001b[39m | \u001b[39m202.11126\u001b[39m | \u001b[39m47.012433\u001b[39m | \u001b[39m35.892027\u001b[39m | \u001b[39m0.4599005\u001b[39m |\n",
      "| \u001b[39m66       \u001b[39m | \u001b[39m0.7806666\u001b[39m | \u001b[39m2783.0677\u001b[39m | \u001b[39m374.11788\u001b[39m | \u001b[39m146.42023\u001b[39m | \u001b[39m17.149585\u001b[39m | \u001b[39m0.6249411\u001b[39m |\n",
      "| \u001b[39m67       \u001b[39m | \u001b[39m0.7798333\u001b[39m | \u001b[39m1241.6715\u001b[39m | \u001b[39m111.95833\u001b[39m | \u001b[39m130.96670\u001b[39m | \u001b[39m39.995680\u001b[39m | \u001b[39m0.3065256\u001b[39m |\n",
      "| \u001b[39m68       \u001b[39m | \u001b[39m0.7809999\u001b[39m | \u001b[39m1591.2669\u001b[39m | \u001b[39m272.05883\u001b[39m | \u001b[39m152.18439\u001b[39m | \u001b[39m89.064828\u001b[39m | \u001b[39m0.7008193\u001b[39m |\n",
      "| \u001b[39m69       \u001b[39m | \u001b[39m0.7805   \u001b[39m | \u001b[39m1738.8556\u001b[39m | \u001b[39m322.31694\u001b[39m | \u001b[39m172.25367\u001b[39m | \u001b[39m14.927934\u001b[39m | \u001b[39m0.6151460\u001b[39m |\n",
      "| \u001b[39m70       \u001b[39m | \u001b[39m0.7826666\u001b[39m | \u001b[39m1105.6256\u001b[39m | \u001b[39m210.30828\u001b[39m | \u001b[39m113.21525\u001b[39m | \u001b[39m26.905757\u001b[39m | \u001b[39m0.5758132\u001b[39m |\n",
      "| \u001b[39m71       \u001b[39m | \u001b[39m0.781    \u001b[39m | \u001b[39m2781.4279\u001b[39m | \u001b[39m33.070318\u001b[39m | \u001b[39m89.106698\u001b[39m | \u001b[39m36.447801\u001b[39m | \u001b[39m0.7891217\u001b[39m |\n",
      "| \u001b[39m72       \u001b[39m | \u001b[39m0.7825   \u001b[39m | \u001b[39m1081.9807\u001b[39m | \u001b[39m455.65269\u001b[39m | \u001b[39m199.17377\u001b[39m | \u001b[39m83.689354\u001b[39m | \u001b[39m0.6619620\u001b[39m |\n",
      "| \u001b[39m73       \u001b[39m | \u001b[39m0.7798333\u001b[39m | \u001b[39m437.66564\u001b[39m | \u001b[39m381.59001\u001b[39m | \u001b[39m154.84757\u001b[39m | \u001b[39m43.105460\u001b[39m | \u001b[39m0.6821938\u001b[39m |\n",
      "| \u001b[39m74       \u001b[39m | \u001b[39m0.7843333\u001b[39m | \u001b[39m747.41700\u001b[39m | \u001b[39m380.14096\u001b[39m | \u001b[39m48.727399\u001b[39m | \u001b[39m12.558982\u001b[39m | \u001b[39m0.4861464\u001b[39m |\n",
      "| \u001b[39m75       \u001b[39m | \u001b[39m0.7741666\u001b[39m | \u001b[39m286.22188\u001b[39m | \u001b[39m149.24865\u001b[39m | \u001b[39m126.63974\u001b[39m | \u001b[39m76.647195\u001b[39m | \u001b[39m0.1949266\u001b[39m |\n",
      "| \u001b[39m76       \u001b[39m | \u001b[39m0.7793333\u001b[39m | \u001b[39m1836.5163\u001b[39m | \u001b[39m18.118982\u001b[39m | \u001b[39m165.95012\u001b[39m | \u001b[39m76.074824\u001b[39m | \u001b[39m0.9598802\u001b[39m |\n",
      "| \u001b[39m77       \u001b[39m | \u001b[39m0.7653333\u001b[39m | \u001b[39m1721.1603\u001b[39m | \u001b[39m5.3941777\u001b[39m | \u001b[39m47.906330\u001b[39m | \u001b[39m35.510318\u001b[39m | \u001b[39m0.1143413\u001b[39m |\n",
      "| \u001b[39m78       \u001b[39m | \u001b[39m0.7776666\u001b[39m | \u001b[39m2858.0536\u001b[39m | \u001b[39m244.36583\u001b[39m | \u001b[39m183.94588\u001b[39m | \u001b[39m57.418499\u001b[39m | \u001b[39m0.3004327\u001b[39m |\n",
      "| \u001b[39m79       \u001b[39m | \u001b[39m0.781    \u001b[39m | \u001b[39m505.73694\u001b[39m | \u001b[39m338.59695\u001b[39m | \u001b[39m163.50715\u001b[39m | \u001b[39m33.129798\u001b[39m | \u001b[39m0.4782533\u001b[39m |\n",
      "| \u001b[39m80       \u001b[39m | \u001b[39m0.7835   \u001b[39m | \u001b[39m556.95900\u001b[39m | \u001b[39m75.486130\u001b[39m | \u001b[39m8.6558649\u001b[39m | \u001b[39m26.756799\u001b[39m | \u001b[39m0.6313273\u001b[39m |\n",
      "| \u001b[39m81       \u001b[39m | \u001b[39m0.7815000\u001b[39m | \u001b[39m1224.9566\u001b[39m | \u001b[39m473.79011\u001b[39m | \u001b[39m143.88174\u001b[39m | \u001b[39m55.109025\u001b[39m | \u001b[39m0.5694877\u001b[39m |\n",
      "| \u001b[39m82       \u001b[39m | \u001b[39m0.7755   \u001b[39m | \u001b[39m223.04664\u001b[39m | \u001b[39m287.96042\u001b[39m | \u001b[39m147.52240\u001b[39m | \u001b[39m46.231769\u001b[39m | \u001b[39m0.2499678\u001b[39m |\n",
      "| \u001b[39m83       \u001b[39m | \u001b[39m0.7516666\u001b[39m | \u001b[39m1807.6947\u001b[39m | \u001b[39m464.98462\u001b[39m | \u001b[39m175.67948\u001b[39m | \u001b[39m64.875943\u001b[39m | \u001b[39m0.1109204\u001b[39m |\n",
      "| \u001b[39m84       \u001b[39m | \u001b[39m0.7735000\u001b[39m | \u001b[39m1247.8515\u001b[39m | \u001b[39m264.10901\u001b[39m | \u001b[39m131.12049\u001b[39m | \u001b[39m77.706472\u001b[39m | \u001b[39m0.1632169\u001b[39m |\n",
      "| \u001b[39m85       \u001b[39m | \u001b[39m0.7815   \u001b[39m | \u001b[39m1382.4313\u001b[39m | \u001b[39m385.53659\u001b[39m | \u001b[39m164.02633\u001b[39m | \u001b[39m91.902852\u001b[39m | \u001b[39m0.5742807\u001b[39m |\n",
      "| \u001b[39m86       \u001b[39m | \u001b[39m0.7806666\u001b[39m | \u001b[39m2694.6705\u001b[39m | \u001b[39m375.58797\u001b[39m | \u001b[39m93.540687\u001b[39m | \u001b[39m48.613531\u001b[39m | \u001b[39m0.8272110\u001b[39m |\n",
      "| \u001b[39m87       \u001b[39m | \u001b[39m0.7833333\u001b[39m | \u001b[39m2745.1147\u001b[39m | \u001b[39m245.38198\u001b[39m | \u001b[39m57.871810\u001b[39m | \u001b[39m17.719007\u001b[39m | \u001b[39m0.8101745\u001b[39m |\n",
      "| \u001b[39m88       \u001b[39m | \u001b[39m0.78     \u001b[39m | \u001b[39m933.15039\u001b[39m | \u001b[39m256.06117\u001b[39m | \u001b[39m22.378530\u001b[39m | \u001b[39m63.350000\u001b[39m | \u001b[39m0.3323077\u001b[39m |\n",
      "| \u001b[39m89       \u001b[39m | \u001b[39m0.779    \u001b[39m | \u001b[39m762.11470\u001b[39m | \u001b[39m308.13330\u001b[39m | \u001b[39m56.013684\u001b[39m | \u001b[39m93.953433\u001b[39m | \u001b[39m0.3625972\u001b[39m |\n",
      "| \u001b[39m90       \u001b[39m | \u001b[39m0.7793333\u001b[39m | \u001b[39m351.78538\u001b[39m | \u001b[39m88.659088\u001b[39m | \u001b[39m3.6657804\u001b[39m | \u001b[39m66.885241\u001b[39m | \u001b[39m0.3511481\u001b[39m |\n",
      "| \u001b[39m91       \u001b[39m | \u001b[39m0.7813333\u001b[39m | \u001b[39m95.248067\u001b[39m | \u001b[39m241.75872\u001b[39m | \u001b[39m23.006006\u001b[39m | \u001b[39m98.411963\u001b[39m | \u001b[39m0.4508095\u001b[39m |\n",
      "| \u001b[39m92       \u001b[39m | \u001b[39m0.7816666\u001b[39m | \u001b[39m2358.9631\u001b[39m | \u001b[39m478.81104\u001b[39m | \u001b[39m193.75648\u001b[39m | \u001b[39m86.070673\u001b[39m | \u001b[39m0.5636019\u001b[39m |\n",
      "| \u001b[39m93       \u001b[39m | \u001b[39m0.7835000\u001b[39m | \u001b[39m2547.9663\u001b[39m | \u001b[39m312.18638\u001b[39m | \u001b[39m72.442860\u001b[39m | \u001b[39m12.083836\u001b[39m | \u001b[39m0.3048117\u001b[39m |\n",
      "| \u001b[39m94       \u001b[39m | \u001b[39m0.7806666\u001b[39m | \u001b[39m2852.6971\u001b[39m | \u001b[39m441.82470\u001b[39m | \u001b[39m141.50327\u001b[39m | \u001b[39m92.639012\u001b[39m | \u001b[39m0.7188809\u001b[39m |\n",
      "| \u001b[39m95       \u001b[39m | \u001b[39m0.7821666\u001b[39m | \u001b[39m475.74481\u001b[39m | \u001b[39m495.72679\u001b[39m | \u001b[39m193.45506\u001b[39m | \u001b[39m90.933108\u001b[39m | \u001b[39m0.6056273\u001b[39m |\n",
      "| \u001b[39m96       \u001b[39m | \u001b[39m0.7828333\u001b[39m | \u001b[39m2109.3185\u001b[39m | \u001b[39m86.258182\u001b[39m | \u001b[39m72.175309\u001b[39m | \u001b[39m50.188768\u001b[39m | \u001b[39m0.4956106\u001b[39m |\n",
      "| \u001b[39m97       \u001b[39m | \u001b[39m0.7823333\u001b[39m | \u001b[39m2729.7666\u001b[39m | \u001b[39m342.63323\u001b[39m | \u001b[39m113.58606\u001b[39m | \u001b[39m43.633311\u001b[39m | \u001b[39m0.5049303\u001b[39m |\n",
      "| \u001b[39m98       \u001b[39m | \u001b[39m0.7808333\u001b[39m | \u001b[39m2749.1099\u001b[39m | \u001b[39m235.85021\u001b[39m | \u001b[39m191.21361\u001b[39m | \u001b[39m54.167328\u001b[39m | \u001b[39m0.5838386\u001b[39m |\n",
      "| \u001b[39m99       \u001b[39m | \u001b[39m0.7823333\u001b[39m | \u001b[39m2564.3120\u001b[39m | \u001b[39m287.11853\u001b[39m | \u001b[39m22.572107\u001b[39m | \u001b[39m67.270207\u001b[39m | \u001b[39m0.4430685\u001b[39m |\n",
      "| \u001b[39m100      \u001b[39m | \u001b[39m0.7801666\u001b[39m | \u001b[39m1236.5262\u001b[39m | \u001b[39m354.04302\u001b[39m | \u001b[39m189.94843\u001b[39m | \u001b[39m94.156661\u001b[39m | \u001b[39m0.7778778\u001b[39m |\n",
      "=====================================================================================\n",
      "                               优化完成！总耗时: 838.83 秒                               \n"
     ]
    }
   ],
   "source": [
    "# 创建贝叶斯优化器，优化的过程已经被这个对象封装了\n",
    "optimizer = BayesianOptimization(\n",
    "    f=rf_eval, # 目标函数\n",
    "    pbounds=pbounds,   # 参数搜索空间\n",
    "    random_state=42,\n",
    "    verbose=2  # 2: 详细信息, 1: 简要信息, 0: 不显示\n",
    ")\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "# 开始优化（大幅增加迭代次数以充分探索超大空间）\n",
    "optimizer.maximize(\n",
    "    init_points=20,  # 初始随机探索点数（增加到20以覆盖超大空间）\n",
    "    n_iter=80        # 贝叶斯优化迭代次数（增加到80）\n",
    ")\n",
    "\n",
    "end_time = time.time()\n",
    "print(f\"优化完成！总耗时: {end_time - start_time:.2f} 秒\".center(80))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4884400",
   "metadata": {},
   "source": [
    "我电脑跑了145min，你们电脑性能一般的不要跑这么久。看cpu性能。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3395a3aa",
   "metadata": {},
   "source": [
    "## 4. 可视化优化过程 📊\n",
    "\n",
    "优化轨迹图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3476d17a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 提取所有迭代的结果\n",
    "iterations = []\n",
    "scores = []\n",
    "for i, res in enumerate(optimizer.res): # res包含每次迭代的结果，index从0开始\n",
    "    iterations.append(i + 1) # 迭代次数从1开始\n",
    "    scores.append(res['target']) # 提取得分\n",
    "\n",
    "# 计算累计最优值\n",
    "best_scores = []\n",
    "current_best = -np.inf # 初始化为负无穷大\n",
    "for score in scores: \n",
    "    if score > current_best: # 检查当前得分是否打破历史记录\n",
    "        current_best = score\n",
    "    best_scores.append(current_best)\n",
    "\n",
    "# 绘制优化轨迹\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 5)) # 创建1行2列的子图\n",
    "\n",
    "# 左图：每次迭代的得分\n",
    "ax1.plot(iterations, scores, 'o-', label='每次迭代得分', alpha=0.7, markersize=6)\n",
    "ax1.plot(iterations, best_scores, 'r--', label='累计最优得分', linewidth=2)\n",
    "ax1.axhline(y=optimizer.max['target'], color='green', linestyle=':', \n",
    "            label=f'最终最优: {optimizer.max[\"target\"]:.4f}') # axhline绘制水平线\n",
    "ax1.set_xlabel('迭代次数', fontsize=12)\n",
    "ax1.set_ylabel('准确率', fontsize=12)\n",
    "ax1.set_title('贝叶斯优化收敛曲线 (超大空间100次迭代)', fontsize=14, fontweight='bold')\n",
    "ax1.legend()\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# 右图：初始探索 vs 贝叶斯优化\n",
    "init_points = 20  # 更新为20\n",
    "ax2.plot(iterations[:init_points], scores[:init_points], 'bo-', \n",
    "         label=f'随机探索 (前{init_points}次)', markersize=8, alpha=0.7)\n",
    "ax2.plot(iterations[init_points:], scores[init_points:], 'go-', \n",
    "         label=f'贝叶斯优化 (后{len(iterations)-init_points}次)', markersize=8, alpha=0.7)\n",
    "ax2.axvline(x=init_points, color='red', linestyle='--', alpha=0.5, label='探索→利用') # axvline绘制垂直线\n",
    "ax2.set_xlabel('迭代次数', fontsize=12)\n",
    "ax2.set_ylabel('准确率', fontsize=12)\n",
    "ax2.set_title('探索阶段 vs 利用阶段', fontsize=14, fontweight='bold')\n",
    "ax2.legend()\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "99eec072",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  总迭代次数: 100\n",
      "  最低得分: 0.7517\n",
      "  最高得分: 0.7847\n",
      "  平均得分: 0.7799\n",
      "  得分标准差: 0.0041\n",
      "  得分提升: 0.0102\n"
     ]
    }
   ],
   "source": [
    "# 输出统计信息\n",
    "print(f\"  总迭代次数: {len(scores)}\")\n",
    "print(f\"  最低得分: {min(scores):.4f}\")\n",
    "print(f\"  最高得分: {max(scores):.4f}\")\n",
    "print(f\"  平均得分: {np.mean(scores):.4f}\")\n",
    "print(f\"  得分标准差: {np.std(scores):.4f}\")\n",
    "print(f\"  得分提升: {max(scores) - scores[0]:.4f}\")"
   ]
  }
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